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  1. Jun 2020
    1. Informal mentorship was captured using the following retrospective question from Wave 3 of the AddHealth data: "Other than your parents or step-parents, has an adult made an important positive difference in your life at any time since you were 14 years old?" Based on this question, I created a binary indicator for mentorship coded 1 if the young person had an informal mentor and 0 if they did not. Respondents were then asked "How is this person related to you?", and given response options like "family,""teacher/counselor,""friend's parent,""neighbor,"and "religious leader.

      Defining informal mentorship in the survey data

    2. Middle-income subsample 3,158

      Middle-income subsample for analysis was 3,158

    3. 1. "Middle-income" is defined as anyone living in a household making two-thirds to double the median income (Pew Research Center, 2016). In 1994, the median income for a family of four was $46,757(US Bureau of Statistics, 1996). Thus, "middle-income" families would be those making between $30,860 and $93,514. Because I only have data available in $25,000 increments, I am defining middle-income families as those making between $25,000 and $100,000 a year in Wave 1.

      Middle-income = families making $25k-$100k a year in Wave 1

    4. Defining low-,middle-, and high-income groupsDue to the limitation in the data described above, all incomes had to be converted in to categorical responses, with the smallest possible category size of $25,000 dollars. This created five categories for all incomes:

      Defining income groups: under $25k, $25k-$49999, $50k-$74999, $75k-$99999, and $100k+.

    5. Wave 1 income was collected as a continuous variable, with an average of $45,728, (N=15,351, SD=$51,616). Low-income respondents (with incomes below $25,000) had an average of $9,837 (N=3,049, SD=4,633). Wave 4 income was recorded as a categorical variable, however, where respondents indicated if they made under $5,000, between $5,000 and $10,000, between $10,000 and $15,000, etc. These categories were of different sizes, getting larger as the income grew larger. Therefore, in order to create comparable measures between Wave 1 and Wave 4, both incomes were converted to 5 groups, (1) household income of less than $25,000, (2) household income of $25,000 to $49,999, (3) household income of $50,000 to $74,000, (4) household income of $75,000 to $99,000, and (5) household income of over $100,000

      Upward mobility (dependent variable); data surrounding household incomes of Wave 1 and Wave 4

    6. stratum. This sampling method yielded a sample of 20,745 students in 7thto 12thgrade, with oversampling of some minority racialethnic groups, students with disabilities, and twins(Harris, 2018). Data were also collected from the parents of the in-home survey respondents, with an 85% success rate (Chen & Chantala, 2014).Wave 1 participants also reported their home address, which was then linked to a number of state-, county-, and Census tract-level variables from other sources. The present study used the school survey data, the in-home interview data, the parent survey data, and the data that was linked to state, county, and census-tracts, as described above. This study also used data from two subsequent waves of in-home interviews, specifically waves 3 and 4 (no new information relevant to the present study was collected in Wave 2). For each subsequent wave, AddHealth survey administrators recruited from the pool of Wave 1 respondents, no matter if they had responded to any wave since Wave 1. The present study used Wave 1 data for information about the youth’s socioeconomic status, social capital and other related variables. This wave collected from 1994 to 1995, when most respondents were between11 and 19 years old (n=20,745 youth) (Harris, 2013).This study also used information from the third wave of in-home interview data, namely all questions on informal mentoring. This wave wascollected in 2001 and 2002 when the youth (N=15,197) were 18 to 26 years old. The fourth wave of data was collected in 2008 and 2009, when the respondents were 25 to 33 years old (n=15,701). Data from the fourth wave wereused to calculate economic mobility, the key dependent variable for this study.

      Data source

    7. DataTo address these questions, this study used three wavesofthe restricted-use version of the National Longitudinal Study of Adolescent Health (AddHealth). AddHealth is a multi-wave longitudinal, nationally representative study of youth who have been followed since adolescence through to adulthood. The AddHealth data were collected by sampling 80 high schools stratified across region, school type, urbanicity, ethnic mix, and school size during the 1994-1995 academic year. Fifty-two feeder schools(commonly middle schools whose students were assumed to go to these study high schools)were also sampled, resulting in a total of 132 sample schools. (Chen & Chantala, 2014, Harris, 2013). When sample high schools had grades 7 to 12, feeder schools were not recruited, as the lower grades served the role of feeding in younger students (Chen, 2014). Seventy nine percent of schools approached agreed to be in the study (Chen & Chantala, 2014). An in-school survey was then administered to over 90,000 students from these 132 schools. This survey was given during a single day within a 45-to 60-minute class period (Chen & Chantala, 2014). Subsequent recruitment for in-home interviews was done by stratifying students in each school by grade and sex and then randomly choosing 17 students from each

      Data source

    8. Figure 1: Potential Ways MentorsCanPromote Mobility

      Figure depicts effects of mentors providing social support and social capital

    9. The third function mentors play in promoting upward mobility for young people is the direct effect the provision of social capital (both bridging and bonding capital) has on building blocks of mobility(Ellwood et al., 2016). Bonding capital from a mentor who is also a teacher could foster feelings of school connectedness, which has been demonstrated to lead to academic engagement and ultimately, educational attainment (Ashtiani & Feliciano, 2018; Li, Lerner, & Lerner, 2010). An employer could have a similar effect by providing bonding capital. If a young person feels connected to the workplace or mission of the work place through their mentoring relationships with their employer, they are likely to have higherjob satisfaction and more opportunities for promotion (Ghosh &Reio 2013). Bridging capital can also have a direct effect on key links in the chain. Studies have shown that bridging mentors (commonly teachers and school personnel) were likely to promote educational attainment and employment

      Social capital (bridging and bonding) can "foster feelings of school connectedness, which has been demonstrated to lead to academic engagement and ultimately, educational attainment"; similar in workplaces, bonding with mentors in settings can create sense of connectedness with setting overall

    10. Those who report feeling emotionally supported have higher rates of academic competence (Sterrett, Jones, Mckee, & Kincaid, 2011) and strong academic outcomes (Wentzel, Russell & Baker, 2016). Additionally, adults who have achieved upward mobility are more likely to report instrumentally supportive relationships than those who were not mobile (Chan, 2017). Clearly, social support has a direct influence on someof thebuilding blocks of mobility

      Social support leads to higher rates of academic competence, strong academic outcomes; has a direct influence on some of the building blocks of mobility

    11. compensate for the lack of other resources their peers have, such as expansive connected social networks.

      Youth from disadvantaged neighborhoods make greater strides than more-resourced peers when mentored by someone outside the family; can potentially compensate for lack of other resources in youth's life

    12. A young person's neighborhood context is associated with their chance of being mentored and their chance of being economically mobile. Young people living in under-resourced neighborhoods are also unlikely to be upwardly mobile (Chetty & Hendren, 2016a; Chetty, & Hendren, 2016b; Chetty, Hendren, Kline & Saez, 2014b; Goldsmith, Britton, Reese, & Velez, 2017). Low-income children are more likely to live in neighborhoods with higher crime and drug use (Abelev, 2009). Young people from these neighborhoods are more likelytohave lower tests scores (McCullock & Joshi, 2001), drop out of high school, and be unemployed (Ainsworth, 2002). This neighborhood effect is cumulative: the more time spent in under
      • Neighborhood is associated with chance of being mentored
      • youth in under-resourced neighborhoods are more unlikely to be upwardly mobile
      • in these neighborhoods, likely to have higher crime and drug rates, lower test scores, drop out of high school, and be unemployed
    13. young people from more advantaged homes and communities as more likely to have an informal mentor.

      Youth in more advantaged homes are more likely to have an informal mentor

    14. Black non-Hispanic youth and girls are most likely to be mentored (Bruce & Bridgeland, 2014) as are youth who have a two-parent home with educated parents (Erickson et al., 2009) and not on public assistance (McDonald & Lambert, 2014). Place matters, as having lived in safe neighborhoods (Miranda-Chan, Fruiht, Dubon, Wray-Lake, 2016) and neighborhoods withhigher rates of white, employed individuals not receiving public assistance and living above the poverty line (McDonald & Lambert, 2014) are all associated with a greater chance of reporting a mentor. A young person’s participation in hobbies, organizations, and religious services also leads to higher rates of informal mentorship (Thompson & Greeson, 2017; Schwartz, Chan, Rhodes, & Scales, 2013). Individual qualities such as prosocial behavior (Hagler, 2017), a secure attachment style (Zinn, Palmer, & Nam, 2017), and a likeable personality (Erickson et al., 2009) are associated with having a natural mentor, as does having more friends

      Typical mentorship demographics

    15. In one study, a low-income child was twice as likely to graduate college when mentored. This is in contrast to previous literature that demonstrates consistent but small associations between informal mentoring and college completion for middle-income children (Reynolds & Parrish, 2018). This suggests that youth from low-income families benefit more from mentorship than those who may have a plethora of positiveresources in their life

      Low-income families benefit more from mentorship; one study suggests that mentored low-income children are 2x as likely to graduate college

    16. For instance, much attention has been paid to informal mentoring and educational outcomes: mentored youth are more likely to feel connected to their school (Black, Grenard, Sussman, & Rohrbach, 2010), have better grades (Chang et al., 2010), attend college (DuBois & Silverthorn, 2005a; Reynolds & Parrish, 2017) and receive a bachelor’s degree (Miranda-Chan, Fruiht, Dubon, & Wray-Lake, 2016; Erickson, McDonald, Elder, 2009). Cumulatively, these studies, along with a 2018 meta-analysis (Van Dam et al.) suggest a strong and consistent relationship between having an informal mentor and positive educational outcomes.

      Informal mentors can result in and influence positive educational outcomes, help promote ability to "feel connected to their school"

    17. Literature has established that informal mentoring is most commonly associated with psychosocial outcomes such as lower stress levels, higher life satisfaction, and lower rates of depression (DuBois & Silverthorn, 2005a; Chang et al., 2010; Munson & McMillen, 2009) and socioemotional outcomes, including improved social skills, perceived social support, and higher self-esteem (Van Dam et al., 2018; Miranda-Chan et al., 2016).These associations are strong and consistent across studies, suggesting that informal mentoring is positively correlated with positive psychosocial and socioemotional outcomes.

      Informal mentoring is positively correlated with positive psychosocial and socioemotional outcomes

    18. Informal mentoring relationships are also more prevalent than formal ones. One study found that 62% of youth had an informal mentoring relationship, compared to just 15% who reportedhaving a formal mentoring relationship(Bruce & Bridgeland, 2014). There are similar differences in prevalence when asking adults if they have mentored young people: 67% of those who reported mentoring someone in the past year did so informally, while only 31% did so through a formal program, (Oosthuizen, 2017). While coming from a low-income family is one of several risk factors associated withlower exposure toinformal mentors, it is clear that many of these youth are still able to identify caring adults in their lives
      • 62% of youth had an informal mentoring relationship
      • 15% reported formal mentoring relationship
      • 67% of adults claimed to have informally mentored someone in last year
      • 31% did so in a formal program
      • even low-income family youth can identify caring adults in their lives
    19. Persistent immobility also disproves the idea of the U.S. being a land of equal opportunity. Since the term "the American Dream" was first coined in 1931, it has become a persistent cultural ethos, a wish list of sorts, with a consistent main tenet being the idea that each generation can achieve more than their parents (Samuel, 2012). Yet we know this tenet of the American Dream is no longer true: the chances that a child earnsmore than their parents has decreased in the past 40 years, especially for low-income families

      chances of earning more than parents has decreased in past 40yrs for low-income families

    20. he associations between childhood poverty andupward mobility are cumulative: each year of childhood spent in poverty lowers an individual's chances of being upwardly mobile, as they are less likely to be consistently employed or in school

      Each year in childhood poverty = less likely to be upwardly mobile, consistently employed/in school

    21. Children who experienced any childhood poverty are less likely to be economically mobilethan their middle-income peers(Chetty et al., 2016c; Mitnik et al., 2015) and are more than five times likelier to remain poor in adulthood than to make it to the top income quintile

      Any childhood poverty = less likely to be economically mobile, 5x likelier to remain poor in adulthood

    22. Even a child who spent just one year in poverty is less likely to have a high school diploma, a key step towards economic success

      1 yr of poverty already = less likely to have a high school diploma

    23. In 2016, 18% of American children were living in poverty, defined fora household of four as living with an annual income of less than $24,755(Semega, Fontenot & Kollar, 2017). Although this is just one snapshot in time, up to 39% of allAmerican children will experience povertyat some point during theirchildhood(Ratcliffe, 2015). Childhood poverty is linked to low educational attainment, socioemotional issues,and development delays. Poor families are likelier to be exposed to food insecurity, homeless, and unsafe neighborhoods. They are also likelier than their middle-income peers to have poorer health and access to health care

      In 2016,

      • 18% of American children lived in poverty
      • poverty = less than $24,755
      • up to 39% of all American children will experience poverty
      • childhood poverty is linked to low educational attainment, socioemotional issues, and development delays
      • poor families more likely to be exposed to food insecurity, homelessness, and unsafe neighborhoods
      • more likely to have poorer health and access to health care
    24. There are over 13 million children and adolescents in poverty in the United States today.

      13 mil children and adolescents live in poverty in US

    25. In 2016, close to one-fifth of American children wereliving in poverty (Semega, Fontenot & Kollar, 2017). These millions of children are likely to remain poor throughout their lives, and are less likely to be upwardly mobile than their middle-income peers (Ratcliffe, 2015; Mitnik, Bryant, Weberb & Grusky, 2015).

      1/5 of American children were living in poverty in 2016; likely to remain poor and less likely to be upwardly mobile

    26. Low-income youth, however, were less likely to have an informal mentor, and only 45% of those who were mentored had the type that could promote mobility.

      Statistical finding: low-income youth likely did not have an informal mentor, and only 45% of those with one were able to have mobility.

    1. Because subject matter expertise goes a long way towards helping you spot interesting patterns in your data faster, the best analysts are serious about familiarizing themselves with the domain. Failure to do so is a red flag. As their curiosity pushes them to develop a sense for the business, expect their output to shift from a jumble of false alarms to a sensibly-curated set of insights that decision-makers are more likely to care about.

      Analysts have domain expertise or knowledge at least.

    2. While statistical skills are required to test hypotheses, analysts are your best bet for coming up with those hypotheses in the first place. For instance, they might say something like “It’s only a correlation, but I suspect it could be driven by …” and then explain why they think that. This takes strong intuition about what might be going on beyond the data, and the communication skills to convey the options to the decision-maker, who typically calls the shots on which hypotheses (of many) are important enough to warrant a statistician’s effort. As analysts mature, they’ll begin to get the hang of judging what’s important in addition to what’s interesting, allowing decision-makers to step away from the middleman role.

      More formal and detailed version of above. Besides, the difference of being important and being interesting should be noted too. Maybe search for a thread.

    3. For example, not “we conclude” but “we are inspired to wonder”. They also discourage leaders’ overconfidence by emphasizing a multitude of possible interpretations for every insight.

      Data analysts are the inspiration team.

    4. Analysts are data storytellers. Their mandate is to summarize interesting facts and to use data for inspiration.

      This is actually what i do in my reviews too, so i may define myself as a qualitative analyst now.

    5. Excellence in analytics: speed The best analysts are lightning-fast coders who can surf vast datasets quickly, encountering and surfacing potential insights faster than those other specialists can say “whiteboard.” Their semi-sloppy coding style baffles traditional software engineers — but leaves them in the dust. Speed is their highest virtue, closely followed by the ability to identify potentially useful gems. A mastery of visual presentation of information helps, too: beautiful and effective plots allow the mind to extract information faster, which pays off in time-to-potential-insights. The result is that the business gets a finger on its pulse and eyes on previously-unknown unknowns. This generates the inspiration that helps decision-makers select valuable quests to send statisticians and ML engineers on, saving them from mathematically-impressive excavations of useless rabbit holes.

      Analysts are more of a digger, they carelessly and fast dig into data, maybe find some directions, which then will be studied elaborately by statisticians and then MLs to create sustainable and automated solutions.

    6. Performance means more than clearing a metric — it also means reliable, scalable, and easy-to-maintain models that perform well in production. Engineering excellence is a must. The result? A system that automates a tricky task well enough to pass your statistician’s strict testing bar and deliver the audacious performance a business leader demanded.

      What machine learners/ AIs do is to scale a statistically rigorous solution to a system-wide, complex problem.

    7. In other words, they use data to minimize the chance that you’ll come to an unwise conclusion.

      Role of statisticians

    1. The p-value says, “If I’m living in a world where I should be taking that default action, how unsurprising is my evidence?” The lower the p-value, the more the data are yelling, “Whoa, that’s surprising, maybe you should change your mind!”

      In a simpler context, it means the occurrence of default (null) situation is of very low probability.

  2. Dec 2019
    1. “Every data point was altered to present the best picture possible,”Bob Crowley | Lessons Learned interview | 8/3/2016Tap to view full document Bob Crowley, an Army colonel who served as a senior counterinsurgency adviser to
  3. May 2019
    1. Brook Lopez this season had more blocks than Kevin Garnett had in his best season and more 3 pointers than Kobe Bryant had in his best season...

      Mindblowing

  4. Apr 2019
    1. There are two tests that you can run that are applicable when the assumption of homogeneity of variances has been violated: (1) Welch or (2) Brown and Forsythe test. Alternatively, you could run a Kruskal-Wallis H Test. For most situations it has been shown that the Welch test is best. Both the Welch and Brown and Forsythe tests are available in SPSS Statistics (see our One-way ANOVA using SPSS Statistics guide).

      ANOVA is robust against violation of the assumption of equal variances, but...

    2. However, platykurtosis can have a profound effect when your group sizes are small. This leaves you with two options: (1) transform your data using various algorithms so that the shape of your distributions become normally distributed or (2) choose the nonparametric Kruskal-Wallis H Test which does not require the assumption of normality.

      ANOVA is robust against violation of normality, but...

  5. Mar 2019
    1. We performed some manipulation checks to examine the internal validity of the perceptual-cognitive skill tests and any learning effects as a result of watching the same video clips multiple times
  6. Feb 2019
    1. Due to our emotional distress measure having little prior validation, and our physical distress measure being entirely new, we first provide data to support the appropriateness of the two measures.

      An example of survey validation using Crombach's alpha.

    1. You may believe that there is a relationship between 10,000 m running performance and VO2max (i.e., the larger an athlete's VO2max, the better their running performance), but you would like to know if this relationship is affected by wind speed and humidity (e.g., if the relationship changes when taking wind speed and humidity into account since you suspect that athletes' performance decreases in more windy and humid conditions).

      An example of partial correlation.

  7. Nov 2017
    1. Developers are an important demographic. Apple says they are the biggest segment of Macbook Pro users, which means they spend a lot of money. And they’re a demographic underserved by Chromebooks today.
    1. Heteroscedasticity

      Heteroscedasticity is a hard word to pronounce, but it doesn't need to be a difficult concept to understand. Put simply, heteroscedasticity (also spelled heteroskedasticity) refers to the circumstance in which the variability of a variable is unequal across the range of values of a second variable that predicts it.

  8. Apr 2017
  9. Mar 2017
  10. Feb 2017
  11. Jan 2017
  12. Feb 2016
    1. He expects that the logging project near Quimby’s land will likely generate about $755,250 at the state’s average sale price, $50.35 per cord of wood. The land has about 1,500 harvestable acres that contain about 30 cords of wood per acre, or 45,000 cords, but only about a third of that will be cut because the land is environmentally sensitive, Denico said. The Bureau of Parks and Lands expects to generate about $6.6 million in revenue this year selling about 130,000 cords of wood from its lots, Denico said. Last year, the bureau generated about $7 million harvesting about 139,000 cords of wood. The Legislature allows the cutting of about 160,000 cords of wood on state land annually, although the LePage administration has sought to increase that amount.
  13. Jan 2016
    1. P(B|E) = P(B) X P(E|B) / P(E), with P standing for probability, B for belief and E for evidence. P(B) is the probability that B is true, and P(E) is the probability that E is true. P(B|E) means the probability of B if E is true, and P(E|B) is the probability of E if B is true.
    2. The probability that a belief is true given new evidence equals the probability that the belief is true regardless of that evidence times the probability that the evidence is true given that the belief is true divided by the probability that the evidence is true regardless of whether the belief is true. Got that?
    3. Initial belief plus new evidence = new and improved belief.
  14. Oct 2013
    1. The things that happen by chance are all those whose cause cannot be determined, that have no purpose, and that happen neither always nor usually nor in any fixed way.

      This is not how statistic work.